Cognitive Systems Research Articles

The Motion of the Mutable and the Constant: An Analytical Study of the Temporal Rotation in the Poetry of Khaled Al-Faisal

This research is based on the conviction that dialect literature carries a cultural and artistic value that demands attention. Categorizing this literature as “popular” or “colloquial,” has been adopted to distinguish it from formal Arabic-language literature, but this research intentionally stays away from these two names, since their implications detract from the value of popular literature and considers it simple and spontaneous consequently, addressing the commoners, based solely on its use of colloquial dialects in direct expression. Surely, oral circulation does not diminish the ability of language to express, nor does it exclude poetry from acquiring the elements of high literature. One of the best representatives of the innovative leap in Nabati dialect poetry is Prince Khaled Al-Faisal, whose tireless effort to activate the mind, and to expand the circle of knowledge, manages successfully to merge the inherited modes of poetic traditions with the vibrant exploration of new patterns of thinking. This paper studies the concept of time in Khaled Al-Faisal’s poetry and his deep reflections on the meaning of the dynamic changes in the cycles of transformation of history. The relationship with the past is one of consolidation and rooting that anticipates the future and prepares for it: every epoch consists of units that interact in a rotational movement: it does not follow a straight linear system of progress from back to front, but rather tends toward the natural cosmic upward spiral movement, whose successive cycles activate the process of filling and emptying, possessing and abandoning, youthfulness and decaying, living and dying. The importance of researching the poetics of time in Khaled Al-Faisal’s poems lies in the fact that such an approach applies a modern and different critical method which traces the philosophy of time in poetry as a new key to reveal the cognitive system which drives the general pattern of his poetic experience. This reading opens up an intellectual and contemplative horizon for us to deal with the essence of time and its paradoxes as manifested in present existence, past retrieval, and futuristic anticipation, three different movements in time that cannot be isolated from the stability of their continuous sequences.

Valence sound symbolism facilitates classification of vowels and emotional facial expressions.

Research on iconicity or sound symbolism chronicles associations between aspects of word meaning and word form. In valence sound symbolism, specifically, vowels were demonstrated to be associated with valence, so that the vowel /i/ (as in English sea) is judged to fit positively valenced stimuli, whereas the vowel /o/ (as in German Boot) is judged to fit negatively valenced stimuli. However, because previous findings were based on judgments or name invention tasks, they may reflect deliberate strategies. The present research aims to demonstrate the automaticity of valence sound symbolism in online language processing by means of a speeded response task. A total of 163 participants performed a speeded categorization task in which they were requested to classify one of two simultaneously presented stimuli, pseudowords containing the vowels /i/ or /o/ and pictures with positive or negative emotional facial expressions. Thus, stimulus pairings could be either congruent (/i/-words and positive expressions; /o/-words and negative expressions) or incongruent (/i/-words and negative expressions; /o/-words and positive expressions). We observed faster and more accurate responses for congruent over incongruent combinations (the simple effects were significant for positive but not for negative expressions). Thus, sound symbolic congruency (vs. incongruency) of an irrelevant stimulus facilitated responses, which accords with an automatic process contributing to valence sound symbolism and indicates that valence sound symbolism is deeply engrained in the human cognitive system. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

GENDER AS A FEATURE OF RECEPTION IN LITERARY STUDIES

The perception of the world, the environment, life, information is a complex mental process consisting of a layer of concepts that are not similar to each other from an individual point of view. Today, «perception» has become a hot topic in various scientific fields. «Perception» is not only a psychological phenomenon, but also a cognitive system realized through a combination of physiological nature, social environment, proper functioning of the cerebral cortex and cognitive processes. The article analyzed the scientific works of domestic and foreign researchers on the topic. We tried to analyze the perception of readers of fiction in the online book platform of Kazakhstan Kitap.kz, dedicated to domestic and foreign literary works. Based on the methods of analysis and experiment carried out on this topic, scientific conclusions were identified that in the future can be used in Literary Studies, cognitive studies, gender differences and in writing dissertations. Keywords: gender, reception, fiction, perception, reading, literary study.

GEXMERT: Geometrically enhanced cross-modality encoder representations from transformers inspired by higher-order visual percepts

Owing to the rapid progress made in transformer-based models and multi-modal fusion strategies, the performance of multi-modal networks has remarkably improved in recent years. However, intrinsic connections among individual modalities are crucial in multi-modal fusion strategies, presenting substantial opportunities for further enhancement of multi-modal perception in this field. Taking inspiration from the biological mechanisms of the human multi-modal cognitive system, we proposed a novel cross-modality fusion network. This network leverages the lateral occipital complex, which is a region responsible for object-shape perception in human vision and integrates an auxiliary modality derived using the conventional multi-stream model. This integration for learning complementary information across image and language modalities effectively captures the biological mechanisms of human processing of higher-order visual percepts through parameter updates of the intermediate modality to optimize the suitability for further fusion. To evaluate the effectiveness of our approach, we conducted experiments under various settings. Our statistical analyses demonstrate a considerable enhancement in the performance of vision-language tasks, thereby validating biological plausibility of our model.

Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated.

Strategies Used by Puerto Rican Children in the Cognitive Assessment System and Their Relationship with Planning Performance.

Studies involving the Cognitive Assessment System (CAS) planning scale typically use only the subtest and scale scores without assessing the strategies employed by the participants. This study addressed this gap and examined the planning strategies used by children in the CAS2: Spanish version and their relationship with planning performance. We conducted an exploratory cross-sectional study with 26 Puerto Rican children aged 8 to 11. Results showed that no strategies were consistently used by participants according to examinees' reports (f = 0-46%), but examiners observed consistent use of some strategies such as "coded left to right, top to bottom", f = 92%; "scanned the page for the next number or letter", f = 100%. Welch's t-tests did not show relationships between participants' performance and the strategies observed by examiners, | mean differences | = 0.05-0.81, ps ≥ 0.05, nor with the strategies reported by participants, | mean differences | = 0.05-1.69, ps ≥ 0.05. These findings suggest that although the examiners may observe the use of strategies, the examinees are unaware of the strategies they use, and the strategies used are not associated with their performance. Future studies are needed to confirm these findings.

How preferences enslave attention: calling into question the endogenous/exogenous dichotomy from an active inference perspective

AbstractIt is easy to think of attention as a purely sensorimotor, exogenous mechanism divorced from the influence of an agent’s preferences and needs. However, according to the active inference framework, such a strict reduction cannot be straightforwardly invoked, since all cognitive and behavioural processes can at least be described as maximising the evidence for a generative model entailed by the ongoing existence of that agent; that is, the minimisation of variational free energy. As such, active inference models could cast an (embodied) cognitive mechanism like attention, described in this paper as a relevance filter, as constrained (or enslaved) by these prior preferences for which an agent must seek evidence, whether or not such priors are having direct, real-time neurocognitive effects on the sensorimotor loops that couple the attending agent and her surrounding environment. This duality with respect to the role of priors corresponds to a wider, ongoing debate in the active inference community regarding the framework’s explanatory power. More specifically, the debate centres on whether the notion of a generative model and the priors embedded ubiqitously therein act as a purely useful instrumental tool for scientists aiming to model the behaviours of self-organising entities, or, rather, the brain (and body) is genuinely constituted by a predictive hierarchy within which higher-order dynamics constrain and contextualise activity unfolding at lower levels. With a focus on the second (ontologically realist) construal of active inference presented here, this paper argues that in cognitive systems endowed with attentional schema, higher-order preferences do, indeed, impose a demonstrable and powerful modulating effect on the way attention unfolds. Furthermore, these preferences in question transcend the contingent, task-relevant goals that have already been shown to bias attention. Rather, attention is powerfully tuned by the most-deep rooted priors the agent possesses, such that, when sensory evidence against these priors is observed and free energy spikes, the agent attentionally prioritises the homeostatic restoration of these preferred states over their shorter-term desires. This suggests that, at its core, attention is a goal-driven process, which calls into question the putative dichotomy that exists between endogenous (goal-directed) attention and exogenous (stimulus-driven) attention. What emerges in its place is a symbiotic relationship between attention and preferences, whereby the fulfilment of the latter rests on successful application of the former, and the former derives its function from the organismic need to find evidence for the latter.

The virtue of ignorance: How epistemic agency needs cognitive limitations

AbstractThe thesis defended in this article is that epistemology should treat some of our cognitive limitations not as unfortunate defects or external perturbations to be idealized away in theories of epistemic agency, but as necessary underpinnings of good reasoning. We begin with a problem regarding deliberation that calls epistemic agency into question: our reasons in support of belief are never conclusive and never rule out all doubt. Yet we must rule out all doubt to close deliberation; we must close deliberation to form a full belief; and we must form a full belief to have knowledge. The problem with the first step calls the whole into question. The solution (if we seek an alternative to rejecting traditional epistemic agency, including the existence of beliefs) is our limitations: they prevent us from considering all possible doubt, leaving a tractable space of possibilities. When these limitations are virtuous, they contribute to an effective cognitive system. Once we understand the role of our limitations, it will lead us to a deeper understanding of deliberation, belief, epistemic virtue, and epistemic agency. Limitations are as much a part of agency as, for example, logical relations are. Idealizing them away means idealizing away actual agency.

Pilot implementation of the revised criteria for staging of Alzheimer's disease by the Alzheimer's Association Workgroup in a tertiary memory clinic.

We aimed to evaluate the feasibility of the 2024 Alzheimer's Association Workgroup's integrated clinical-biological staging scheme in outpatient settings within a tertiary memory clinic. The 2018 syndromal cognitive staging system, coupled with a binary biomarker classification, was implemented for 236 outpatients with cognitive concerns. The 2024 numeric clinical staging framework, incorporating biomarker staging, was specifically applied to 154 individuals within the Alzheimer's disease (AD) continuum. The 2024 staging scheme accurately classified 95.5% AD. Among these, 56.5% exhibited concordant clinical and biological stages (canonical), 34.7% demonstrated more advanced clinical stages than biologically expected (susceptible), and 8.8% displayed the inverse pattern (resilient). The susceptible group was characterized by a higher burden of neurodegeneration and inflammation than anticipated from tau, whereas the resilient group showed the opposite. The 2024 staging scheme is generally feasible. A discrepancy between clinical and biological stages is relatively frequent among symptomatic patients with AD. The 2024 AA staging scheme is generally feasible in a tertiary memory clinic. A discrepancy between clinical and biological stages is relatively frequent in AD. The mismatch may be influenced by a non-specific pathological process involved in AD. Individual profiles like aging and lifestyles may contribute to such a mismatch. Matched and mismatched cases converge toward similar clinical outcomes.

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

Digital Industry transformation strategy analysis and decision-based on intelligent decision technology

Expanding and being competitive in the current economic environment requires companies to embrace digital transformation. In the framework of Industry 4.0, the network of interconnected machines, sensors, and software known as the IIoT plays a crucial role in transforming conventional manufacturing facilities into smart factories, notably in monitoring and optimising the manufacturing process. The issues about enormous record storage and how they react challenge conventional automated methods in the IIoT. Cognitive systems optimally modify production settings based on managing uncertainty and sensory inputs. This work uses the Internet of Things-based decision support system with cognitive automation (IoT-DSS-CA) for industrial informatics across the board, including data collection, transmission, processing, and storage. Incorporating the elements frequently neglected during digital transformation, the suggested method uses the business process management (BPM) paradigm to give a systematic approach that industrial organizations may employ to aid their path towards Industry 4.0. The proposed mechanism is thoroughly investigated and evaluated compared to an original solution using several sensing and decision-making features in industrial parameter settings determined by Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP).

Exploring the Mechanisms that Allow Incompatible Beliefs to Coexist in the Cognitive System.

Despite assumptions that people strive for consistency between their beliefs, endorsement of mutually incompatible ones is not rare - a tendency we have previously labelled doublethink, by borrowing from Orwell. In an attempt to understand the nature of doublethink and the underlying mechanism that enables incompatible beliefs to coexist, we conducted two preregistered studies (total N = 691). To do so, in Study 1, we first explored how doublethink relates to (1) thinking styles (rational/intuitive, actively open-minded thinking, and need for cognitive closure), (2) a set of irrational beliefs (magical health, conspiratorial, superstitious, and paranormal beliefs) and (3) its predictiveness for questionable health practices (non-adherence to medical recommendations and use of traditional, complementary and alternative medicine). We then additionally expanded the set of health behaviors in Study 2, and related doublethink to trust in two epistemic authorities - science and the wisdom of the common man. Finally, in both studies, we explored whether those prone to inconsistent beliefs are also more likely to simultaneously rely on conventional and alternative medicine, despite their apparent incompatibility. While doublethink was positively related to need for cognitive closure and different irrational beliefs that easily incorporate contradictions, as well as negatively to actively open-minded thinking, we did not find it to be predictive of the use of non-evidence-based medicine nor of its simultaneous use with official medicine. It seems that this novel construct can be best understood as a feature of the cognitive system that allows incompatible claims to enter it. However, once beliefs are within the system, they are compartmentalized, without any cross-referencing between them. This is further reflected in non-evidence-based beliefs persisting within the belief system, irrespective of their content.

Implementation of automated cognitive testing systems for socially housed rhesus (Macaca mulatta) and squirrel (Saimiri spp.) monkeys: Age differences in learning.

Utilizing Automated Cognitive Testing Systems (ACTS) with group-housed nonhuman primates offers a number of advantages over manual testing and computerized testing of singly housed subjects. To date, ACTS usage has been limited to great apes or African monkeys. Here, we detail what we have learned while implementing ACTS with socially housed squirrel monkeys and rhesus macaques and provide information about the training process. In addition, we examined the effects of age on learning acquisition. We found age differences in learning for both squirrel monkeys and rhesus monkeys. Older monkeys were not as proficient as younger monkeys on learning to use the touch screens (squirrel monkeys only), discrimination learning (rhesus monkeys only; note: squirrel monkeys were not trained to criterion on this task), and recognition learning (both species). Overall, ACTS provide a number of advantages for studying cognition in socially living nonhuman primates and can be used to further investigate cognitive decline whether related to natural aging processes or disease pathology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Harmonising humans and technology: Exploring the dynamics of cognitive production, artificial intelligence and social communication in cybernetic systems

Agile cognitive production systems mark a manufacturing paradigm shift, propelled by the demand for accelerated product development and the adoption of digitalised production systems across extensive supply networks. Cognitive manufacturing emphasises the role of technology and automation in the learning and adaptation process. These systems independently analyse data, make real-time adjustments and optimise processes, sometimes minimising the need for human intervention. Based on a conceptual framework that draws on the diversity of living systems and cognitive processes, cybernetics provides a solid theoretical background. It explores the intricate connections between cognition, self-organising systems and the challenges arising from the autonomy of such systems. The concept of "cognition" in "agile cognitive systems" moves away from the conventional understanding of purely technical processes and towards human thought processes. This departure fosters a dynamic exchange where individual thoughts resonate in social communication. Addressing the role of artificial intelligence (AI), the article emphasises examining computers from a social science standpoint, exploring the relationship between computers and mental systems, capturing human faculties such as cognition, utterance, and understanding. The integration of AI into computer-mediated communication leads to the question how AI-equipped computers intersect with societal intelligence notions. The inherent intransparency of AI, often viewed as a black box, prompts queries about the potential black-box nature of an autonomously controlled AI factory or supply chain. In this hypothetical scenario, the idea of the supply chain as a communication network is challenged, emphasising the importance of human involvement. Research on human-centric cognitive production emphasises explainable AI and human-in-the-loop. This orientation goes beyond the technical dimensions and incorporates social science considerations, which emphasises the holistic nature of current research. In essence, research in the field of cognitive production is a comprehensive exploration of the complex interplay between human cognition, artificial intelligence and the evolving landscape of modern production systems.

Analysis of delays in medium access control devices in cognitive radio systems with correlation of time intervals in request flows

This article is about the development of a method for analyzing the average waiting time of a request in a queue with correlated inter-arrival and service times. The ON-OFF process is often used to model the primary user channel occupancy in different questions related to cognitive radio, such as resource allocation, medium access control protocols, spectrum handoff and others. It is assumed that the time intervals of the ON- and OFF- periods are not correlated. More realistic models of primary user activity require the presence of correlation not only between consequent periods, but also the presence of correlation between channel occupancy and other quantities interpreted as service time in models based on queuing systems. As a result, here is proposed a variant of constructing the probability density function of a random variable which is the difference between the service time of the i-th request and the time interval between the moments of arriving of the i-th and (i-1)-th requests with correlated inter-arrival and service times. Solution based on the use of a two-dimensional characteristic function, so in this case it is possible to more purposefully specify the components of the statistical connection of previously described sequences. These components are the cumulants of the described distribution. For a queuing system with hyperexponential distribution of inter-arrival and service intervals, an analytical expression for the average waiting time of a request in a queue is found.

Understanding the relationships among adolescents’ internet dependence, reward, cognitive control processing, and learning burnout: a network perspective in China

Alterations in the reward and cognitive control systems are commonly observed among adolescents with internet dependence (ID), and this impairment is often accompanied by social dysfunctions, such as academic burnout. However, the intercorrelations among ID, reward, cognitive control processing, and learning burnout remain unclear. We recruited 1074 Chinese adolescents to investigate the complex interrelationships among these variables using network analysis. The resulting network revealed patterns that connected ID to the behavioral inhibition/activation system (BIS/BAS), self-control, and learning burnout; these results exhibited reasonable stability and test–retest consistency. Throughout the network, the node of BAS-drive was the critical influencing factor, and the node of self-control was the protection factor. In addition, several symptoms of learning burnout and ID were positively associated with sensitivity to punishment. As revealed by the network comparison test, the network constructed among internet dependent (ID) group differed from the network constructed among internet nondependent (IND) group not only in the edges between BIS and learning burnout but also in terms of the edges associated with learning burnout. In conclusion, this study provides insights into the complex mechanisms underlying ID among adolescents from the perspective of the network relationships between core influencing factors and negative consequences. It validates the dual-system model of risky behavior among adolescents and offers a foundation for early warning and interventions for ID in this context.

A smart system for student performance assessment (SPA)

The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Functional connectivity of the pediatric brain.

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders. The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents. Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders. Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

The impact of anger on intertemporal decision-making in individuals with internet addiction: an fNIRS study.

Intertemporal decision-making is the choice between an immediate smaller reward (SS) and a delayed larger reward (LL). Intertemporal decision-making depends on the interaction of the cognitive and emotional systems, and the latter is particularly vital. According to the Appraisal Tendency Frame (ATF) theory, anger influences intertemporal decision-making by increasing an individual's sense of certainty and control. This study examined whether anger affects intertemporal decision-making in individuals with internet addiction (IA) in this manner and investigated its neural mechanisms. Nineteen individuals with IA and 20 healthy controls were recruited. All subjects performed the Monetary choice task under anger and neutral emotions while functional near-infrared spectroscopy (fNIRS) equipment simultaneously recorded the hemodynamics in the prefrontal cortex (PFC). Individuals with IA showed a more considerable delay discount and lower brain activations in the orbitofrontal cortex (OFC) and left dorsolateral prefrontal cortex (L-dlPFC) compared to HC. Moreover, individuals with IA made more LL choices in the angry condition than in the neutral emotion, yet there was no difference in HC. The brain activation in L-dlPFC of individuals with IA tends to increase in the angry condition compared to the neutral condition. These findings revealed that impairment of intertemporal decision-making in individuals with individuals with IA might be related to the dysfunction of OFC and L-dlPFC. Our work also provided initial footing for the applicability of the appraisal tendency frame theory to individuals with IA, and L-dlPFC might play a role in the effects of anger on intertemporal decision-making.

Exploring Cognitive Skills and Academic Outcomes of Poverty-Affected English Language Learners in Indian Primary Schools

This study investigates the cognitive performance of primary school children in India who are English Language Learners (ELL) and live in poverty. Cognitive performance was assessed using the Das-Naglieri Cognitive Assessment System (CAS2 was not constructed). There is a scarcity of studies on participants who are both ELL and live in poverty, and many such children are often found to be intellectually deficient. This study examines the performance in PASS measures (Planning, Attention, Simultaneous, Successive) and their correlation with academic achievement, particularly in reading and mathematics. We assessed the performance of 80 primary school students residing in slum areas (mean age: 9.8 years), matched in age and grade with 70 regular school students from areas outside the slum (mean age: 9.11 years). It was hypothesized that ELL regular school students would exhibit average Das-Naglieri CAS scores as per established American norms.